run.sh

基于稀疏网络的精选机器学习模型

#! /bin/csh -f
#
# SNoW usage example: Training on simple boolean examples
#
# The training examples used for this example are the same as those given in
# the user guide for the disjunction x2 v x5.  Using 9 examples of the
# disjunction and two cycles over these examples, SNoW learns the concept with
# 93.75% accuracy after two rounds of training.  In a simple boolean example
# like this, it turns out that a single Winnow learner will perform better
# than two Winnows in the SNoW architecture.  To confirm this, uncomment both
# of the "set" commands that are commented out below and run this script
# again.  Since only one target is specified in the one_target.arch
# architecture file, SNoW will make predictions as classical Winnow does:
# comparing the target's activation to the specified threshold.  With the
# parameters specified in one_target.arch, classical Winnow learns the concept
# with 100% accuracy over the entire six feature space after two rounds of
# training.

# Note the use of an eligibility threshold of 1.  This is important in small
# experiments such as this one, where some important features are much more
# likely to only appear once with a target in the training examples.  Also
# note the -g- option, which disables the automatic generation of
# conjunctions.  Finally, note the use of the "allactivations" output mode,
# allowing us to see results at a more detailed level than the default.

echo "Testing SNoW using the boolean example data..."
echo ""

set train_file=two_targets_train.snow
#set train_file=one_target_train.snow
set test_file=booleantest.snow

set net_file=boolean.net
set arch_file=two_targets.arch
#set arch_file=one_target.arch
set results_file=boolean.out

echo "../snow -train -I $train_file -F $net_file -A $arch_file -e count:1 -g-"
../snow -train -I $train_file -F $net_file -A $arch_file -e count:1 -g-

echo ""
echo "../snow -test -I $test_file -F $net_file -o allboth -R $results_file"
../snow -test -I $test_file -F $net_file -o allboth -R $results_file